Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
I Thought I Understood my Dataset - Then I Met Row 142,794
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For the 4 1/2 years of my PhD, I worked with a de-identified dataset that felt like nothing more than numbers on a page. Cold. Abstract. Disconnected from any real human experience. Each “person” was just a line in an Excel spreadsheet, with an ID in place of a name. When I started my first role in insurance pricing, my mindset initially remained the same. That was until my boss took me along to speak to a policyholder - putting me face-to-face with one of the people my data actually represented. I sat across from a man who was running a business that was struggling to make ends meet - one of the few Australian manufacturing companies that was adamant their product would continue to remain Australian-made. The premium increase that I didn’t think twice about when performing the calculation was causing him genuine financial distress. As he told his story, I could see him blinking back tears at times. That day, everything changed for me. I realised my data was more than just numbers - it represented actual human beings, with emotions and struggles, who behave in sometimes unpredictable ways. This lesson has became even more relevant as I’ve watched our world become increasingly app-driven. Today, understanding the human behaviour behind every click, scroll, and purchase has become absolutely critical for business success. That’s exactly what we dive into in the latest episode of Value Driven Data Science, where I’m joined by Miguel Curiel, Product Analytics Manager at Bloomberg. In our conversation, Miguel breaks down:
Miguel is currently writing his own book on product analytics, so you’re getting insights from someone literally writing the playbook on this emerging field. If you’ve ever wondered how Netflix knows exactly what to recommend next, or how companies like Bloomberg optimize their digital products, this episode pulls back the curtain on the human psychology driving those decisions. Listen now on Apple Podcasts or Spotify, or click the link below: Episode 84: The 7-Step Checklist for Creating Business Impact Through Product Analytics Talk again soon, Dr Genevieve Hayes |
Data Science Impact Algorithm
Twice weekly, I share proven strategies to help data scientists get noticed, promoted, and valued. No theory — just practical steps to transform your technical expertise into business impact and the freedom to call your own shots.
When I started my career, data science didn’t exist as a field. I trained as an actuary and statistician and those were the tools I relied on in my earliest roles. Then, around 10 years ago, I started hearing about the wonders of machine learning and became worried that my traditional training was no longer enough. So, despite already having a PhD in Statistics, I went back and completed a Masters in Machine Learning. Then came the AI wave – ChatGPT, large language models, generative AI – and...
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